SETR [1]} provide a semantic segmentation framework for standard Vision Transformer. Hence, we adopt the SETR as the semantic segmentation strategy on Cityscapes [2]}. Cityscapes contains 5000 images, with 19 object categories annotated in pixel level. There are 2975, 500, and 1525 images in training, validation, and testing set respectively. We follow the training config as original SETR. For fair comparison, we both use the 300-epoch pretrained model for DINO and our method.
